Transcript-Free Flow-Matching Text-to-Speech via Speech Feature Conditioning

SooHwan Eom; Hee Suk Yoon; Eunseop Yoon; Mark Hasegawa-Johnson; Chang D. Yoo

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arXiv · 2606.20266

Transcript-Free Flow-Matching Text-to-Speech via Speech Feature Conditioning

SooHwan Eom·Hee Suk Yoon·Eunseop Yoon·Mark Hasegawa-Johnson·Chang D. Yoo

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Abstract

Recent flow-matching text-to-speech (TTS) models, such as F5-TTS, rely on a reference transcript at inference time, obtained from an external ASR system. This dependency makes zero-shot TTS brittle for accented or dysarthric speakers, precisely the scenarios where it is most needed. Moreover, we find that text-based reference conditioning can propagate atypical acoustic patterns from atypical speech into synthesis, even when ground-truth transcripts are available. To address this, we propose RTFree-F5, which replaces the reference transcript with continuous self-supervised speech representations mapped into F5-TTS's text-conditioning space via a lightweight adapter, while reusing the pretrained checkpoint. On dysarthric speech, RTFree-F5 reduces WER from 24.6% to 10.4%, surpassing even the ground-truth reference transcript baselines, while improving naturalness and remaining competitive on standard benchmarks without requiring any reference transcript.

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BibTeX RIS

SooHwan Eom, Hee Suk Yoon, Eunseop Yoon, Mark Hasegawa-Johnson, Chang D. Yoo. 2026-06-18. Transcript-Free Flow-Matching Text-to-Speech via Speech Feature Conditioning. https://arxiv.org/abs/2606.20266

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